Future Models Research Articles

Going back for the future: Incorporating Pleistocene fossil records of saiga antelope into habitat suitability models

AbstractAimMany species have suffered anthropogenic range contraction and no longer occupy all available suitable environmental conditions. This is particularly problematic for the construction of habitat suitability models (HSMs), which assume that a species' contemporary range reflects its full species–environment relationship. HSMs therefore risk underestimating suitable environment areas, and misinforming conservation decisions. Incorporating historic (centuries‐old) records partly reduces this bias, but even these records are also subject to human disturbance. We incorporated fossil records of the critically endangered saiga antelope (Saiga tatarica, L., 1776), alongside historic and current records, into current and future habitat suitability models. Saiga has experienced drastic range contraction and may have a truncated species–environment relationship. The results allowed us to test whether its current habitat provides optimal environmental conditions, or whether saiga should be considered a refugee species.LocationNorthern Hemisphere.TaxonSaiga tatarica (Bovidae, Artiodactyla).MethodsWe collated historic and fossil saiga occurrence records from published literature, museum archives and global databases. Modern occurrence records were obtained from the International Union for Conservation of Nature Red List assessment. Four bioclimatic variables were downloaded from Worldclim.org. HSMs were generated through Maxent, using the maxnet package in R. Three HSMs were developed: present only, present historic and present fossil. Each of these models was projected onto current and two future (2070) climate change scenarios.ResultsSaiga fossil records increased the predicted suitable environment area by 783% and 1416% for current and future climate projections respectively. Our results suggest the saiga is not a refugee species but occupies only a portion of its potential environmental niche. The saiga's contemporary range is predicted environmentally suitable throughout all models and projections, and therefore in situ conservation management is recommended.Main ConclusionsThis study highlights the importance of incorporating fossil records into HSMs to better understand species–environment relationships and develop more robust conservation strategies for appropriate endangered species.

Modelling foraging strategies of honey bees as agents in a dynamic landscape representation

Introduction: Both intrinsic colony mechanisms and external environmental variables affect the honey bee colony development rates and response and a key aspect of this is the use of resources within the landscape by honey bees. Although several models have been developed to explore the foraging behaviour of bees, none fully considered the spatial and temporal dynamics of landscape resources and the role of the colony in the process. Methodology: Here, we introduce a new honey bee foraging model being developed as a part of the ApisRAM honey bee colony model. Based on agent-based modelling, we used a dynamic ALMaSS landscape model enhanced with floral resource modelling to assess the impacts of weather conditions and resource availability on the possible foraging behaviour of honey bees. Several possible mechanisms (defined, based on honey bee traits) for scouting and foraging were investigated, separately for nectar and pollen collection, including prioritising foraging polygons for nectar foraging according to their distance to the colony, the quality or the energetic efficiency and, for pollen foraging, according to their distance to the colony and pollen quantity. Results: If model foraging bees prioritised the polygons, based on their distance from the colony, the number of unsuccessful flights increased compared to other tested strategies and the total amount of sugar collected showed a high variability. Contrary to expectations, the energetic efficiency strategy did not provide the colony with the highest amount of sugar. Overall, the tested strategies provide different outcomes on the collection of resources, the number of performed flights and their success rate, evidencing that the model's outcome at the colony level arises from the individual types of behaviour. Conclusions and Relevance: Variability in the mass of collected nectar and pollen was found mostly when scout bees applied the distance strategy. This higher variability in sugar collection means that model bees were not able to find the most profitable foraging sites at the landscape level, but only at the local level. Other strategies showed less dependence on the surrounding landscape (i.e. quality or random), but it comes at a cost (i.e. lower production for both nectar and pollen collection). These outputs help us evaluate which strategies could be used for future model development and confirm the models' ability to create dynamic responses. These responses at the colony level were only possible thanks to the implementation of a dynamic landscape model and dynamic spatiotemporal resource model, as well as implementing a social communication mechanism for bees to share information about the resources. Plant nectar production and quality information is essential to predict honey bee foraging distribution. In future model development, the implementation of pollen quality should also be explored to evaluate if it influences the overall pollen collection.

Structural and practical identifiability analysis in bioengineering: a beginner’s guide

Advancements in digital technology have brought modelling to the forefront in many disciplines from healthcare to architecture. Mathematical models, often represented using parametrised sets of ordinary differential equations, can be used to characterise different processes. To infer possible estimates for the unknown parameters, these models are usually calibrated using associated experimental data. Structural and practical identifiability analyses are a key component that should be assessed prior to parameter estimation. This is because identifiability analyses can provide insights as to whether or not a parameter can take on single, multiple, or even infinitely or countably many values which will ultimately have an impact on the reliability of the parameter estimates. Also, identifiability analyses can help to determine whether the data collected are sufficient or of good enough quality to truly estimate the parameters or if more data or even reparameterization of the model is necessary to proceed with the parameter estimation process. Thus, such analyses also provide an important role in terms of model design (structural identifiability analysis) and the collection of experimental data (practical identifiability analysis). Despite the popularity of using data to estimate the values of unknown parameters, structural and practical identifiability analyses of these models are often overlooked. Possible reasons for non-consideration of application of such analyses may be lack of awareness, accessibility, and usability issues, especially for more complicated models and methods of analysis. The aim of this study is to introduce and perform both structural and practical identifiability analyses in an accessible and informative manner via application to well established and commonly accepted bioengineering models. This will help to improve awareness of the importance of this stage of the modelling process and provide bioengineering researchers with an understanding of how to utilise the insights gained from such analyses in future model development.

Numerical model of pressure generated by elastic compression garments on a compressible human limb analogue.

This study aimed to formulate a numerical approach (finite element modelling (FEM)) to calculate pressure values generated by compression garments on a compressible limb analogue, and to validate the numerical approach using experimental measurements. Existing models were also compared. Experimentally measured pressure values and deformation caused by compression bands on a compressible human limb analogue were compared with values predicted using the Young-Laplace equation, a previously formulated analytical model and the FEM. The FEM provided greater accuracy in predicting the pressure generated by compression bands compared to existing models. The FEM also predicted deformation of the limb analogue with good agreement relative to experimental values. It was concluded that modelling the non-uniform manner in which the way a limb analogue is compressed should be incorporated into future modelling of the pressures generated by compression garments on a compressible limb analogue. The authors have no conflicts of interest to declare.

Framework for the Classification of Facial Emotions Using Soft Computing Techniques

Background:: Facial emotion recognition (FER) technology is enumerated as a productive interface in several operations, which has been specifically focused on as a substitute communication path among a user and an appliance for human computer interface in the previous decade. The efficiency of the facial identification model straightaway relies on the capability of classification methods. In addition, an appropriate swap between recognition efficiency and computational cost is reckoned as the most important factor for planning such models. Methods:: The objective of this paper was to classify the facial emotion electromyogram (EMG) signals by means of a neural network algorithm (NN), support vector machine (SVM) algorithm, and Naive-Bayes algorithm. This research work was directed towards the correlation among the classification accuracies by applying distinct feature extraction procedures on fEMGs. At first, eight participants (six male and two female) were recruited for data recording. Four electrodes were placed on each participant's face for capturing facial gestures (happy, angry, sad, and fear) and two electrodes were placed on the wrist for grounding purposes. Data were recorded by using BIOPAC MP150. After this, the signals were filtered using a band-pass filter and segmentation techniques for enhanced processing. After that, the time-domain and frequency-domain feature extraction procedures were carried out. Time domain and frequency domain features were applied to recorded signals. In this research, we used LabVIEW and MATLAB to produce a set of characteristics from fEMG signals for four emotional conditions, such as anger, sad, fear, and happy. After the feature extraction process, the extracted features were aligned into respective emotions by applying classifiers. The extracted features were further trained and classified by applying the SVM classifier, neural network classifier, and Naive Bayes classifier in MATLAB 2020. Results:: The SVM classifier and neural network classifier generated an accuracy of 93.80% and 96.90%, respectively, whereas the Naive Bayes classifier generated an accuracy of 90.60%. Conclusion:: Facial emotion recognition (FER) is foresighted as a progressive or futuristic model, which has attracted the attention of researchers in several areas of learning due to its higher prospects in distinct applications. Acknowledgment of the emotions through biomedical signals produced from movements of facial muscles is lately presented using an explicit and authentic route.

Biochemical and metabolic characterization of a G6PC2 inhibitor

Three glucose-6-phosphatase catalytic subunits, that hydrolyze glucose-6-phosphate (G6P) to glucose and inorganic phosphate, have been identified, designated G6PC1-3, but only G6PC1 and G6PC2 have been implicated in the regulation of fasting blood glucose (FBG). Elevated FBG has been associated with multiple adverse clinical outcomes, including increased risk for type 2 diabetes and various cancers. Therefore, G6PC1 and G6PC2 inhibitors that lower FBG may be of prophylactic value for the prevention of multiple conditions. The studies described here characterize a G6PC2 inhibitor, designated VU0945627, previously identified as Compound 3. We show that VU0945627 preferentially inhibits human G6PC2 versus human G6PC1 but activates human G6PC3. VU0945627 is a mixed G6PC2 inhibitor, increasing the Km but reducing the Vmax for G6P hydrolysis. PyRx virtual docking to an AlphaFold2-derived G6PC2 structural model suggests VU0945627 binds two sites in human G6PC2. Mutation of residues in these sites reduces the inhibitory effect of VU0945627. VU0945627 does not inhibit mouse G6PC2 despite its 84% sequence identity with human G6PC2. Mutagenesis studies suggest this lack of inhibition of mouse G6PC2 is due, in part, to a change in residue 318 from histidine in human G6PC2 to proline in mouse G6PC2. Surprisingly, VU0945627 still inhibited glucose cycling in the mouse islet-derived βTC-3 cell line. Studies using intact mouse liver microsomes and PyRx docking suggest that this observation can be explained by an ability of VU0945627 to also inhibit the G6P transporter SLC37A4. These data will inform future computational modeling studies designed to identify G6PC isoform-specific inhibitors.

The Historical Process and Deep Influence of Sino-Thai Educational Intermingling—A Case Study on Yunnan Context

Background and Aims: As China's radiation center faces South and Southeast Asia and the forefront of the "the Belt and Road" construction, Yunnan's unique geographical location and rich cultural heritage provide valuable opportunities for educational exchanges between the two countries. Therefore, to further analyze the profound impact of cultural exchange between the two countries on China. This article aims to explore the future direction and possibilities of education cooperation between China and Thailand, taking Yunnan as an example. From a more comprehensive and profound perspective, it provides feasible ways to further strengthen the relationship between the two countries and deepen cooperation. Methodology: By using the methods of historical records, policy analysis, and comparative study, this paper examines the evolution of educational exchanges between China and Thailand and its impact on the educational policies of the two countries. This paper first reviews the historical evolution of educational exchanges between China and Thailand, and analyzes the cooperation mechanism and contents of cooperation between the two countries in the field of education. Then, this paper compares the characteristics of the internationalization of higher education between China and Thailand, as well as the cooperation between the two countries in personnel training, teaching method improvement, and education quality assurance. Finally, this paper discusses the impact of Sino-Thai educational exchanges on the economic, cultural, and educational changes of the two countries, as well as the experiences and feelings of students in educational exchanges between the two countries. Results: the educational cooperation between China and Thailand has developed from the early language and cultural exchanges to a deeper level of educational cooperation, including degree education and professional training. This paper finds that the driving force of Sino-Thai educational cooperation mainly comes from the political, economic, and cultural interdependence of the two countries, as well as the interest and curiosity of the students of the two countries. This paper also finds that the effect of Sino-Thai educational cooperation is mainly reflected in the improvement of the knowledge, skills, and literacy of the students of the two countries, as well as the enhancement of cross-cultural awareness and cross-cultural communication ability of the students of the two countries. Conclusion: The educational exchanges between China and Thailand have not only enriched the learning experience of the students of the two countries but also promoted friendly cooperation and exchanges between the two countries in the field of education. This paper holds that the educational exchange between China and Thailand is not only an important way for cultural exchange between the two countries but also an important basis for the friendly relations between the two countries. This paper suggests that the future Sino-Thai educational cooperation model should pay more attention to the actual needs of the market and society, as well as the development of high-level cooperative education. This paper also suggests that the two countries should strengthen the supervision and evaluation of educational exchanges to ensure the quality and effectiveness of education.

Key Factors Determining Nightside Energetic Electron Losses Driven by Whistler‐Mode Waves

AbstractEnergetic electron losses by pitch‐angle scattering and precipitation to the atmosphere from the radiation belts are controlled, to a great extent, by resonant wave particle interactions with whistler‐mode waves. The efficacy of such precipitation is primarily modulated by wave intensity, although its relative importance, compared to other wave and plasma parameters, remains unclear. Precipitation spectra from the low‐altitude, polar‐orbiting ELFIN mission have previously been demonstrated to be consistent with energetic precipitation modeling derived from empirical models of field‐aligned wave power across a wide swath of local‐time sectors. However, such modeling could not explain the intense, relativistic electron precipitation observed on the nightside. Therefore, this study aims to additionally consider the contributions of three modifications—wave obliquity, frequency spectrum, and local plasma density—to explain this discrepancy on the nightside. By incorporating these effects into both test particle simulations and quasi‐linear diffusion modeling, we find that realistic implementations of each individual modification result in only slight changes to the electron precipitation spectrum. However, these modifications, when combined, enable more accurate modeling of ELFIN‐observed spectra. In particular, a significant reduction in plasma density enables lower frequency waves, oblique, or even quasi field‐aligned waves to resonate with near ∼1 MeV electrons closer to the equator. We demonstrate that the levels of modification required to accurately reproduce the nightside spectra of whistler‐mode wave‐driven relativistic electron precipitation match empirical expectations and should therefore be included in future radiation belt modeling.

Do they stay, or do they go? Children presenting to five emergency departments across New South Wales, Australia with acute burn injuries: a retrospective review

ObjectiveThe overall objective of the study was to describe the disposition status of children presenting with a burn injury to five emergency departments (ED) across New South Wales (NSW), Australia.DesignA...

Insights and Challenges for the Development of Mosquito Control Action Thresholds Using Historical Mosquito Surveillance and Climate Datasets.

Strategies to advance action threshold development can benefit both civilian and military vector control operations. The Anastasia Mosquito Control District (AMCD) has curated an extensive record database of surveillance programs and operational control activities in St. Johns County, Florida, since 2004. A thorough exploratory data analysis was performed on historical mosquito surveillance and county-wide climate data to identify climate predictors that could be used in constructing proactive threshold models for initiating control of Aedes, Culex, and Anopheles vector mosquitoes. Species counts pulled from Centers for Disease Control and Prevention (CDC) light trap (2004-2019) and BG trap (2014-2019) collection records and climate parameters of temperature (minimum, maximum, average), rainfall, and relative humidity were used in two iterations of generalized linear models. Climate readings were incorporated into models 1) in the form of continuous measurements, or 2) for categorization into number of "hot," "wet," or "humid" days by exceedance of selected biological index threshold values. Models were validated with tests of residual error, comparison of model effects, and predictive capability on testing data from the two recent surveillance seasons 2020 and 2021. Two iterations of negative binomial regression models were constructed for 6 species groups: container Aedes (Ae. aegypti, Ae. albopictus), standing water Culex (Cx. nigripalpus, Cx. quinquefasciatus), floodwater Aedes (Ae. atlanticus, Ae. infirmatus), salt-marsh Aedes (Ae. taeniorhyncus, Ae. sollicitans), swamp water Anopheles (An. crucians), and a combined Total Mosquitoes group. Final significant climate predictors varied substantially between species groups. Validation of models with testing data displayed limited predictive abilities of both model iterations. The most significant climate predictors for floodwater Aedes, the dominant and operationally influential species group in the county, were either total precipitation or frequency of precipitation events (number of "wet" days) at two to four weeks before trap collection week. Challenges hindering the construction of threshold models were discussed. Insights gained from these models provide initial feedback for streamlining the AMCD mosquito control program and analytical recommendations for future modelling efforts of interested mosquito control programs, in addition to generalized guidance for deployed armed forces personnel with needs of mosquito control but lacking active surveillance programs.

The relationship between niche breadth and phylogenetic characteristics of eight species of rhubarb on the Qinghai-Tibet Plateau, Asia.

The relationship between spatial distribution and phylogeny has been widely debated in recent decades. To understand biogeographic and evolutionary history relationships and to explore the interspecific similarities and phylogenetic correlations of niche characteristics, we collected and recorded all distribution points for eight species of rhubarb on the Qinghai-Tibet Plateau, used different methods to describe the ecological niche, and explored the relationship between phylogeny, ecological niche, and distribution range. The results reveal that: (1) the ranges of optimal environmental variables for species with close kinship are not exactly the same, ecologically similar species are not necessarily sister species, and the overlap of rhubarb has no significant correlation with phylogeny. Therefore, the impact of ecological dimensions on species formation is greater than that of geographical latitude for the eight species of rhubarb. (2) Among the eight species of rhubarb, the breadth of ecological niche is positively correlated with the current suitable habitat area and negatively correlated with fluctuations in future suitable habitat area. In the future, except for Rheum tanguticum and Rheum palmatum, the suitable planting areas for the other six species of rhubarb will decrease as greenhouse gas emissions concentrations and time increase. Therefore, species with smaller ecological niches are at a greater risk of habitat loss compared to species with larger ecological niches. (3) In both existing and future distribution prediction models of rhubarb, we observed that both the widely distributed Rheum spiciforme and the niche narrow Rheum nobile, all eight species of rhubarb are present in the Hengduan Mountains, based on our analysis, we propose that the Hengduan Mountains should be regarded as a priority conservation area for rhubarb, to preserve the species' biodiversity. Our study lays the groundwork for identifying evolutionary trends in ecological specialization.

Predicting Cloud Conditions in Substellar Mass Objects Using Ultracool Dwarf Companions

We present results from conducting a theoretical chemical analysis of a sample of benchmark companion brown dwarfs whose primary star is of type F, G, or K. We summarize the entire known sample of these types of companion systems, termed “compositional benchmarks,” that are present in the literature or recently published as key systems of study in order to best understand brown dwarf chemistry and condensate formation. Via mass balance and stoichiometric calculations, we predict a median brown dwarf atmospheric oxygen sink of 17.8−2.3+1.7% by utilizing published stellar abundances in the local solar neighborhood. Additionally, we predict a silicate condensation sequence such that atmospheres with bulk Mg/Si ≲0.9 will form enstatite (MgSiO3) and quartz (SiO2) clouds, and atmospheres with bulk Mg/Si ≳0.9 will form enstatite and forsterite (Mg2SiO4) clouds. The implications of these results on C/O ratio trends in substellar-mass objects and the utility of these predictions in future modeling work are discussed.

Authorized and Unauthorized Consumption of SVOD Content: Modeling Determinants of Demand and Measuring Effects of Enforcing Access Control

Consumers are attracted by the increasing number of available SVOD platforms, but it would be too expensive to pay the subscription fees for all of them. To reduce costs, consumers can combine the use of proprietary subscriptions, non-proprietary subscriptions, and illegal streaming sites. In turn, platforms could enforce access control, a decision that might produce the desired reduction in non-proprietary subscriptions but also an undesired reduction in proprietary subscriptions. The effects of this decision and the determinants of SVOD content demand remain largely unexplored. We propose a baseline model where the SVOD content demand is driven by variety seeking, household financial situation, ethical evaluation, and social norms, as well as a change model where the subscription variation is driven by users’ trait reactance and perceived fairness of the decision. We conducted a survey on the current ways SVOD content is consumed and responses to a hypothetical access control enforcement, with four randomized versions of the authentication mode. Results confirmed many of the proposed determinants and showed a noteworthy reduction in proprietary subscriptions due to the control enforcement but no effect due to the authentication modes. All these findings may help improve future models of SVOD content consumption and better address the difficult challenge of converting unauthorized users into authorized ones.

The Vision of Spanish Schools in Post-Pandemic Times

Extraordinary measures were required due to Covid-19, such as confinement and emergency distance education. With health regulations in place, the schools then returned to face-to-face instruction and used the lessons they had learned to improve the teaching standard. The study's intention is to investigate how Spain's schools are evolving post-confinement, with a particular interest in the digitalization of schools, teaching competence and future models of education. Method: The approach was qualitative; the instrument was a semi-structured interview, constructed ad hoc by experts and applied by trained interviewers. The sample consisted of 75 principals or members of management teams from primary and secondary schools in Spain. We carried data coding and analysis out using Atlas.ti software. Results: The confinement led to difficulties in adapting teaching to the online modality. Using the pandemic as a catalyst, schools were able to quickly move to digitalization, expand teacher training in digital technology with varying levels of compliance, and introduce new methodologies such as active learning strategies. Conclusions: Schools in Spain have introduced changes in their technological processes and equipment and slight methodological aspects. Teachers require more training on teaching skills, especially digital skills. Finally, the traditional face-to-face model is valued but is questioned and alternatives are considered.

The Perceived Influence Model of Trust: Toward a Multi-Trustee Theory

Prior research investigating situations involving one trustor and multiple trustees often examines how a trustor’s trust in one party affects their amount of trust in another party. This paper fills a gap by predicting the effects of trust. The Perceived Influence (PI) Model of Trust is an individual-level model focused on the perceptions of a trustor. It builds upon the Mayer et al. (1995) model by integrating insights from literature on task interdependence and expanding to two trustees. The PI model describes and explains three possibilities for how a trustor’s trust in two trustees may combine to form a sense of aggregate multi-trustee trust via: (1) additive effects, such that the trustor’s trust in each of the trustees has independent effects on the aggregate; (2) compulsory effects, such that increasing the amount of trust in one trustee increases the effect of trust in the other trustee; and (3) compensatory effects, such that increasing trust in one trustee decreases the effect of trust in the other trustee. We propose that the theoretical mechanism explaining which of these three possibilities takes place is the trustor’s perceived influence of the trustees, which is tightly linked to perceptions of task requirements necessary to attenuate the trustor’s risk. The PI model begins to fill an important gap in the literature pertaining to pervasive, but rarely considered, multi-trustee situations, and proposes the importance of trustor perceptions of trustee influence and task requirements for future models of trust.

A Review on the Mullins Effect in Tough Elastomers and Gels

AbstractTough elastomers and gels have garnered broad research interest due to their wide-ranging potential applications. However, during the loading and unloading cycles, a clear stress softening behavior can be observed in many material systems, which is also named as the Mullins effect. In this work, we aim to provide a complete review of the Mullins effect in soft yet tough materials, specifically focusing on nanocomposite gels, double-network hydrogels, and multi-network elastomers. We first revisit the experimental observations for these soft materials. We then discuss the recent developments of constitutive models, emphasizing novel developments in the damage mechanisms or network representations. Some phenomenological models will also be briefly introduced. Particular attention is then placed on the anisotropic and multiaxial modeling aspects. It is demonstrated that most of the existing models fail to accurately predict the multiaxial data, posing a significant challenge for developing future anisotropic models tailored for tough gels and elastomers.

A Critical Review of Risk Assessment Models for Listeria monocytogenes in Seafood.

Invasive listeriosis, due to its severe nature in susceptible populations, has been the focus of many quantitative risk assessment (QRA) models aiming to provide a valuable guide in future risk management efforts. A review of the published QRA models of Listeria monocytogenes in seafood was performed, with the objective of appraising the effectiveness of the control strategies at different points along the food chain. It is worth noting, however, that the outcomes of a QRA model are context-specific, and influenced by the country and target population, the assumptions that are employed, and the model architecture itself. Studies containing QRA models were retrieved through a literature search using properly connected keywords on Scopus and PubMed®. All 13 QRA models that were recovered were of short scope, covering, at most, the period from the end of processing to consumption; the majority (85%) focused on smoked or gravad fish. Since the modelled pathways commenced with the packaged product, none of the QRA models addressed cross-contamination events. Many models agreed that keeping the product's temperature at 4.0-4.5 °C leads to greater reductions in the final risk of listeriosis than reducing the shelf life by one week and that the effectiveness of both measures can be surpassed by reducing the initial occurrence of L. monocytogenes in the product (at the end of processing). It is, therefore, necessary that future QRA models for RTE seafood contain a processing module that can provide insight into intervention strategies that can retard L. monocytogenes' growth, such as the use of bacteriocins, ad hoc starter cultures and/or organic acids, and other strategies seeking to reduce cross-contamination at the facilities, such as stringent controls for sanitation procedures. Since risk estimates were shown to be moderately driven by growth kinetic parameters, namely, the exponential growth rate, the minimum temperature for growth, and the maximum population density, further work is needed to reduce uncertainties.

Evaluating the Effectiveness of Best Management Practices in Adapting the Impacts of Climate Change-Induced Urban Flooding

Floods are amongst the most destructive and costly natural disasters impacting communities around the globe. The severity and reoccurrence of flooding events have been more common in recent years as a result of the changing climate and urbanization. Best Management Practices (BMPs) are commonly used flood management techniques that aim to alleviate flooding and its impacts by capturing surface runoff and promoting infiltration. Recent studies have examined the effectiveness of BMPs in countering the effects of flooding; however, the performance of such strategies still needs to be analyzed for possible future climate change. In this context, this research employs climate model-driven datasets from the North American Regional Climate Change Assessment Program to evaluate the effects of climate change on urban hydrology within a study region by calculating historical and projected 6 h 100-year storm depths. Finally, the climate-induced design storms are simulated in the PCSWMM model, and the three BMP options (i.e., porous pavement, infiltration trench, and green roof) are evaluated to alleviate the impact of flooding events. This study quantifies the impact of changing climate on flood severity based on future climate models. The results indicate that peak discharge and peak volume are projected to increase by a range of 5% to 43% and 8% to 94%, respectively. In addition, the results demonstrated that green roofs, Permeable Pavement, and infiltration trenches help to reduce peak discharge by up to 7%, 14%, and 15% and reduce flood volume by up to 19%, 24%, and 29%, respectively, thereby presenting a promising solution to address the challenges posed by climate change-induced flooding events.

Impact of locational pricing on the roll out of heat pumps in the UK

This paper investigates the impact of locational pricing on the roll out of heat pumps in the UK. Qualitative assessment of proposals set out for electricity market reform in the UK identified locational pricing as potentially having an impact on heat pump running costs. Energy system modelling was used to assess the economics of both individual heat pumps and system-wide heat pump roll out under both unified pricing and locational pricing for the UK for 2020 and 2035. PyPSA-GB, a future power system model, was employed to simulate unified and locational wholesale prices and the Octopus Agile tariff was used to calculate indicative retail tariffs applicable to domestic heat pumps. The research highlights that locational pricing can create market conditions which better reflect the true cost of generating and delivering electricity, however, it can also lead to higher heat pump operating costs in regions with projected high heat demand. Key findings reveal that locational pricing could result in significant geographical disparities in heat pump operating costs due to varying electricity prices across different zones. Further work is required to develop policy to alleviate high operating costs and to promote flexible operation of heat pumps.

Interpretable corn future price forecasting with multivariate time series

AbstractEfforts in corn future price forecasting and early warning play a vital role in guiding the high‐quality development of the agricultural economy. However, recent years have witnessed significant fluctuations in global corn future prices due to the impact of COVID‐19 and the escalating risks associated with geopolitical conflicts. Therefore, there is an urgent need for accurate and efficient methods to forecast corn future prices. To address this challenge, a novel and comprehensive framework for explainable corn future price forecasting is designed. This framework takes into account multiple factors contributing to corn price volatility, including supply and demand dynamics, policy adjustments, international market shocks, global geopolitical risks, and investor concerns within the corn market. During the data processing stage, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to thoroughly explore the volatility characteristics of historical corn future prices. Additionally, a convolutional neural network (CNN) is employed to extract essential forecasting information from corn news data. To enhance interpretability, a novel JADE–TFT interpretable corn future price prediction model is proposed. This model combines adaptive differential evolution with optional external archiving (JADE) to intelligently and efficiently optimize the parameters of the temporal fusion transformers (TFTs). Furthermore, in the empirical study, the introduction of a global geopolitical risk coefficient, Baidu indices such as “corn” and “corn price,” and quantized corn news text features is shown to improve the accuracy of corn future price predictions. The proposed corn future price prediction framework contributes to the healthy development of the global grain futures market, thereby fostering the growth and well‐being of enterprises involved in the grain industry.